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kimi-k3
📰 Tech Blog | 📄 Full Report
## 1. Model Introduction
Kimi K3 is an open-weight, native multimodal agentic model and our most capable model to date. It is a 2.8T-parameter model built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), with native vision capabilities and a 1-million-token context window. It is the world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning.
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Repository: localaiLicense: other
kat-coder-v2.5-dev
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-q8
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry uses the higher-quality Q8_0 GGUF quantization.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-quality
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-balanced
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-compact
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-mini
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-quality
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-balanced
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-compact
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
inkling
# Inkling
BF16 |
NVFP4 |
Playground |
Tinker Cookbook |
Acceptable Use
## 1. General Information
Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.
**Languages:** English, with general multilingual capabilities across other languages.
## 2. Getting Started
Try Inkling on the Tinker Playground or access via API using the Tinker Cookbook.
Inkling supports local deployment using the following open-source libraries:
* SGLang (recipe, PR)
* vLLM (recipe, PR)
* TokenSpeed (recipe, PR)
* Unsloth (recipe, PR)
* Huggingface (recipe, PR)
...
Repository: localaiLicense: apache-2.0
qwythos-9b-v2
Empero AI
# Qwythos-9B-v2 — the new and improved Qwythos
The next iteration of Qwythos: **all the reasoning of Qwythos-9B, with the looping behavior fixed.** v2 keeps the deep chain-of-thought, the uncensored research posture, and the 1M-token context of its predecessor, and cleans up the rough edges that showed up in real use.
- 🔁 **Looping behavior eliminated** — repetition/degeneration under greedy or low-temperature decoding dropped from **6.7% → 0%**. You can serve it *without* leaning on `repetition_penalty` as a band-aid.
- 🧠 **Reasoning fully preserved** — MMLU, GSM8K, GPQA, ARC and HumanEval are all held at (or above) the v1 level. This is a *hygiene* upgrade, not a capability regression.
- 🧩 **MTP head restored** — the native multi-token-prediction module (dropped in the previous export) is back, so config and weights agree and speculative-decoding setups work.
- 🪪 **Cleaner identity** — the model no longer prefaces unrelated answers with its identity; it introduces itself only when you actually ask.
- 🔓 **Still intentionally uncensored** for research, cybersecurity, red-teaming, biology, chemistry, pharmacology and clinical work.
- 📜 **St
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Repository: localaiLicense: apache-2.0
minicpm5-1b-claude-opus-fable5-v2-thinking
# MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
GGUF quantizations for local deployment: **MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF**
中文说明
**MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking** is a compact 1B **Thinking** language model built on openbmb/MiniCPM5-1B. Compared with V1, this V2 release is further fine-tuned on **Fable 5** data with a stronger focus on **tool calling / function calling**, while also improving **coding** and **instruction-following**. It keeps MiniCPM5's native Thinking chat template and XML tool-call format.
Previous version: **MiniCPM5-1B-Claude-Opus-Fable5-Thinking** (V1)
For llama.cpp / Ollama / LM Studio deployment, see the **GGUF repository**.
## Overview
## Capabilities
- **Tool calling (enhanced in V2)** — more reliable XML / function-calling style tool use on top of MiniCPM5's native format
- **Coding** — code generation, debugging, and software-engineering-style tasks
- **Instruction following** — more reliable adherence to user prompts and structured constraints
- **Thinking mode** — chain-of-thought reasoning via the MiniCPM5 chat template
- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
...
Repository: localaiLicense: apache-2.0
bonsai-27b-1bit
Bonsai 27B (PrismML) is a full 27B-class reasoning model in end-to-end 1-bit weights,
derived from the Qwen3.6-27B hybrid-attention backbone (~75% linear attention, 262K
context). At a true 1.125 bits/weight it deploys in ~3.9 GB (~14.2x smaller than FP16)
while retaining 89.5% of FP16 intelligence across 15 thinking-mode benchmarks (math
91.66, coding 81.88). Ships an optional 4-bit vision tower (mmproj) for image input,
included here.
The Q1_0_g128 weights and hybrid-attention kernels are only in the PrismML llama.cpp
fork, so this runs on LocalAI's `bonsai` backend. A GPU is recommended. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
minicpm5-1b-claude-opus-fable5-thinking
# MiniCPM5-1B-Claude-Opus-Fable5-Thinking
GGUF quantizations for local deployment: **MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF**
中文说明
**MiniCPM5-1B-Claude-Opus-Fable5-Thinking** is a compact 1B **Thinking** language model built on openbmb/MiniCPM5-1B. It is further fine-tuned on **Fable 5** data to improve **coding** and **instruction-following** while keeping MiniCPM5's native Thinking chat template and tool-call format.
For llama.cpp / Ollama / LM Studio deployment, see the **GGUF repository**.
## Overview
## Capabilities
- **Coding** — code generation, debugging, and software-engineering-style tasks
- **Instruction following** — more reliable adherence to user prompts and structured constraints
- **Thinking mode** — chain-of-thought reasoning via the MiniCPM5 chat template
- **Tool calling** — inherits MiniCPM5's XML tool-call format
- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
## Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking"
...
Repository: localaiLicense: apache-2.0
qwopus3.6-35b-a3b-coder-mtp
# 🌟 Qwopus3.6-35B-A3B-v1
## 💡 Base Model Overview
**Qwen3.6-35B-A3B** is an advanced hybrid sparse MoE (Mixture-of-Experts) model developed by Alibaba Cloud. It features 35B total parameters with only 3B active parameters per token, ensuring high inference efficiency. Architecturally, it combines Gated DeltaNet linear attention with standard gated attention layers, routing tokens across **256 experts**. It natively supports a massive **262k context window** and is specifically designed for high-performance agentic coding, deep reasoning, and multimodal tasks.
## 🚀 Model Refinement & Logic Tuning （Qwopus3.6-35B-A3B-v1）
🪐**Qwopus3.6-35B-A3B-v1** is a reasoning-enhanced MoE (Mixture of Experts) model fine-tuned on top of **Qwen3.6-35B-A3B**.
### 🛠 Training Strategy
The fine-tuning process for this model is structured into **three distinct stages of distributed SFT (Supervised Fine-Tuning)**, progressively scaling reasoning complexity and data diversity. This systematic approach ensures the model inherits the base MoE capabilities while sharpening its logic-handling depth.
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Repository: localaiLicense: apache-2.0
ornith-1.0-9b-mtp
[](https://deep-reinforce.com/ornith.html)
# Ornith-1.0-9B
Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.
Highlights:
- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
- **Self-Improving Training Framework**: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.
- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
## Ornith 1.0 9B
This model card documents **Ornith-1.0-9B**, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment.
### Benchmarks
Ornith-1.0-9B
Qwen3.5-9B
Qwen3.5-35B
Gemma4-12B
Gemma4-31B
Agentic Coding
...
Repository: localaiLicense: mit
ornith-1.0-9b
[](https://deep-reinforce.com/ornith.html)
# Ornith-1.0-9B-GGUF
Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.
Highlights:
- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
- **Self-Improving Training Framework**: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.
- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
## Ornith 1.0 9B
This model card documents **Ornith-1.0-9B**, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment.
### Benchmarks
Ornith-1.0-9B
Qwen3.5-9B
Qwen3.5-35B
Gemma4-12B
Gemma4-31B
Agentic Coding
...
Repository: localaiLicense: mit
ornith-1.0-35b
[](https://deep-reinforce.com/ornith.html)
# Ornith-1.0-35B-GGUF
Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.
Highlights:
- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
- **Self-Improving Training Framework**: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.
- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
## Ornith 1.0 35B
This model card documents **Ornith-1.0-35B**, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.
### Benchmarks
Ornith-1.0-35B
Qwen3.5-35B
Qwen3.6-35B
Gemma4-31B
Qwen3.5-397B
Agentic Coding
...
Repository: localaiLicense: mit
gemmable-4-12b-mtp
## Gemmable 4 12B
Gemmable 4 12B is a GGUF export of Gemma 4 12B fine-tuned on Fable-5 style
reasoning and assistant traces.
## Highlights
- Base model: `google/gemma-4-12B`
- Format: GGUF
- Training style: Fable-5 style reasoning and assistant traces
- Distribution: fp16 GGUF plus matching assistant GGUFs for each quant
- Intended use: local inference, coding, reasoning, and assistant workflows
## How to use
### llama.cpp
Standard load:
```bash
llama-server -m "gemmable-4-12b-fp16.gguf"
```
Speculative / draft-MTP load:
```bash
llama-server -m "gemmable-4-12b-Q4_K_M.gguf" \
--spec-draft-model "gemmable-4-12b-Q4_K_M-mtp.gguf" \
--spec-type draft-mtp \
--spec-draft-n-max 4
```
Use the matching fp16 or quantized main file with its `-mtp` companion.
### LM Studio
1. Search this repo, download target + mtp file.
2. Load target.
3. Load settings → Speculative Decoding → select mtp file file.
(Requires a llama.cpp runtime with Gemma 4 MTP support from ggml-org/llama.cpp#23398. LocalAI's pinned llama.cpp backend already carries it, so this entry runs draft-mtp out of the box.)
## GGUF / local inference notes
...
Repository: localai
laguna-xs-2.1
Laguna XS 2.1 is Poolside's 33B-parameter, 3B-active Mixture-of-Experts model for agentic coding and long-horizon work on local machines. It supports tool use, interleaved reasoning, and a native 262K-token context window. This default entry uses the official 20.3 GB Q4_K_M GGUF.
License: OpenMDW 1.1.
Repository: localaiLicense: other
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